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Eva Krieghoff-Henning (1976-)

Biologin

Name	Eva Krieghoff-Henning
Zeit	1976- (Lebensdaten)
Ort	Bonn (Geburtsort) Deutschland
Akademischer Grad bzw. Titel	Dr.
Beruf	Biologin (charakteristischer Beruf)
Variante	Eva I. Krieghoff-Henning Eva Isabelle Krieghoff-Henning Eva Krieghoff Eva Krieghoff- Henning Eva Isabelle Krieghoff (früherer Name)
Beziehung	Deutsches Krebsforschungszentrum
Weitere Angaben	Diss. Universität Erlangen-Nürnberg, Naturwissenschaftliche Fakultät II - Biologie, Chemie, Pharmazie
Datensatztyp	Person
GND-Nummer	132407914
Datensatz-ID	132407914
Beteiligt an alle anzeigen Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma Combining CNN-based histologic whole slide image analysis and patient data to improve skin cancer classification Evaluating deep learning-based melanoma classification using immunohistochemistry and routine histology: A three center study Integrating Patient Data Into Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review Robustness of convolutional neural networks in recognition of pigmented skin lesions Colorectal cancer risk stratification on histological slides based on survival curves predicted by deep learning A self-supervised vision transformer to predict survival from histopathology in renal cell carcinoma Künstliche Intelligenz auf dem Vormarsch – Hohe Vorhersage-Genauigkeit bei der Früherkennung pigmentierter Melanome Künstliche Intelligenz auf dem Vormarsch – Hohe Vorhersage-Genauigkeit bei der Früherkennung pigmentierter Melanome Künstliche Intelligenz auf dem Vormarsch – Hohe Vorhersage-Genauigkeit bei der Früherkennung pigmentierter Melanome Deep learning can predict survival directly from histology in clear cell renal cell carcinoma Diagnostic performance of artificial intelligence for histologic melanoma recognition compared to 18 international expert pathologists Model soups improve performance of dermoscopic skin cancer classifiers A benchmark for neural network robustness in skin cancer classification Combining CNN-based histologic whole slide image analysis and patient data to improve skin cancer classification Deep learning can predict lymph node status directly from histology in colorectal cancer Gastrointestinal cancer classification and prognostication from histology using deep learning : Systematic review Hidden Variables in Deep Learning Digital Pathology and Their Potential to Cause Batch Effects: Prediction Model Study Integrating Patient Data Into Skin Cancer Classification Using Convolutional Neural Networks : Systematic Review Integration of deep learning-based image analysis and genomic data in cancer pathology: A systematic review Skin cancer classification via convolutional neural networks : systematic review of studies involving human experts Effects of Label Noise on Deep Learning-Based Skin Cancer Classification Analyzing the regulation of the subcellular localization of β-catenin [beta-catenin] by fluorescence recovery after photobleaching Analyzing the regulation of the subcellular localization of β-catenin [beta-catenin] by fluorescence recovery after photobleaching

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Katalog der Deutschen Nationalbibliothek

Eva Krieghoff-Henning (1976-)

Dermatologist-like explainable AI enhances trust and confidence in diagnosing melanoma

Combining CNN-based histologic whole slide image analysis and patient data to improve skin cancer classification

Evaluating deep learning-based melanoma classification using immunohistochemistry and routine histology: A three center study

Integrating Patient Data Into Skin Cancer Classification Using Convolutional Neural Networks: Systematic Review

Robustness of convolutional neural networks in recognition of pigmented skin lesions

Colorectal cancer risk stratification on histological slides based on survival curves predicted by deep learning

A self-supervised vision transformer to predict survival from histopathology in renal cell carcinoma

Künstliche Intelligenz auf dem Vormarsch – Hohe Vorhersage-Genauigkeit bei der Früherkennung pigmentierter Melanome

Künstliche Intelligenz auf dem Vormarsch – Hohe Vorhersage-Genauigkeit bei der Früherkennung pigmentierter Melanome

Künstliche Intelligenz auf dem Vormarsch – Hohe Vorhersage-Genauigkeit bei der Früherkennung pigmentierter Melanome

Deep learning can predict survival directly from histology in clear cell renal cell carcinoma

Diagnostic performance of artificial intelligence for histologic melanoma recognition compared to 18 international expert pathologists

Model soups improve performance of dermoscopic skin cancer classifiers

A benchmark for neural network robustness in skin cancer classification

Combining CNN-based histologic whole slide image analysis and patient data to improve skin cancer classification

Deep learning can predict lymph node status directly from histology in colorectal cancer

Gastrointestinal cancer classification and prognostication from histology using deep learning : Systematic review

Hidden Variables in Deep Learning Digital Pathology and Their Potential to Cause Batch Effects: Prediction Model Study

Integrating Patient Data Into Skin Cancer Classification Using Convolutional Neural Networks : Systematic Review

Integration of deep learning-based image analysis and genomic data in cancer pathology: A systematic review

Skin cancer classification via convolutional neural networks : systematic review of studies involving human experts

Effects of Label Noise on Deep Learning-Based Skin Cancer Classification

Analyzing the regulation of the subcellular localization of β-catenin [beta-catenin] by fluorescence recovery after photobleaching

Analyzing the regulation of the subcellular localization of β-catenin [beta-catenin] by fluorescence recovery after photobleaching